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L'effet de visites manquantes sur l'estimateur des GEE, une étude par simulation

Abstract : Clinical research is regularly interested in longitudinal follow-up over several visits. All scheduled visits are not carried out and it is not unusual to have a different number of visits by patient. The Generalized Estimating Equations can handle continuous or discrete autocorrelated response. The method allows a different number of visits by patients. The GEE are robust to missing completely at random data. However when the last visits are fewer, the estimator may be biased. We propose a simulation study to investigate the impact of missing visits on the GEE estimators under different missing data pattern. Different types of responses are studied with an exchangeable or autore-gressive of order one structure. The number of subjects affected by the missing data and the number of visits removed vary in order to assess their impact. Our simulations show that the estimators obtained by GEE are resistant to a certain rate of missing data. The results are homogeneous regardless to the imposed missing data structure.
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Julia Geronimi, Gilbert Saporta. L'effet de visites manquantes sur l'estimateur des GEE, une étude par simulation. 47èmes journées de statistique, Jun 2015, Lille, France. ⟨hal-02507494⟩

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